Discovering Latent Causes and Memory Modification: A Computational Approach Using Symmetry and Geometry

Published: 29 Nov 2023, Last Modified: 29 Nov 2023NeurReps 2023 PosterEveryoneRevisionsBibTeX
Submission Track: Proceedings
Keywords: computational cognitive science, symmetry, geometry, algebra, algorithm, latent causes, memory modification, unsupervised learning, categorization, artificial intelligence
TL;DR: Latent Causes and Memory Modification by Symmetries
Abstract: We learn from our experiences, even though they are never exactly the same. This implies that we need to assess their similarity to apply what we have learned from one experience to another. It is proposed that we “cluster” our experiences based on hidden latent causes that we infer. It is also suggested that surprises, which occur when our predictions are incorrect, help us categorize our experiences into distinct groups. In this paper, we develop a computational theory that emulates these processes based on two basic concepts from intuitive physics and Gestalt psychology using symmetry and geometry. We apply our approach to simple tasks that involve inductive reasoning. Remarkably, the output of our computational approach aligns closely with human responses.
Submission Number: 31